Studies Evaluate Medications, Biomarkers Linked to GCA Risk

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11/11/2022

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Findings from two studies published in Rheumatology suggest that angiotensin receptor blocker (ARB) use may be associated with an increased risk for developing giant cell arteritis (GCA), and that interferon (IFN)-γ may warrant further investigation as a risk biomarker.

For the first study, Sarah Mackie (University of Leeds, UK) and team investigated the incidence of GCA in over a million people initiating treatment with antihypertensive drugs in UK primary care between 1995 and 2019. A total of 957 individuals developed GCA during 5 years of follow-up.

In a weighted analysis accounting for potentially confounding factors, the researchers found that people on ARBs (n=74,390) had a significant 55% higher risk for GCA than those taking angiotensin-converting enzyme (ACE) inhibitors (n=73,902), with incidence rates of 2.73 versus 1.76 per 10,000 patient–years. Rates of GCA were comparable among people initiating other antihypertensives (n=75,712) and those on ACE inhibitors, however.

A similar pattern of results was seen for polymyalgia rheumatica (PMR). The risk was a significant 15% higher with ARBs than ACE inhibitors, whereas it was comparable for other antihypertensive agents and ACE inhibitors.

These findings suggest that “[n]on-ARB drugs may be preferable when choosing antihypertensives (or other indications for ACE [inhibitors] and ARB) in patients with increased risk of GCA and/or PMR, such as older patients with a personal or family history of autoimmunity,” write Mackie and colleagues.

They emphasize, however, that “the priority should always be effective treatment of hypertension with whichever agent is suitable for that patient.”

The second study, from Karin Wadström (Lund University, Malmö, Sweden) and co-authors, investigated whether biomarkers of inflammation were associated with the development of GCA among 30,447 people who were included in the Malmö Diet Cancer Study in 1991–1996.

A total of 94 people developed incident GCA after a median of 11.9 years. In an analysis of eight biomarkers that were preselected for evaluation, the researchers identified IFN-γ as a possible biomarker for GCA development.

They say that IFN-γ levels measured in the blood at baseline were significantly higher among people who developed GCA relative to 97 matched controls, at 7.27 versus 7.07 units, giving an odds ratio (OR) of 1.52 per standard deviation increase.

When patients were categorized into four groups according to time from baseline to GCA diagnosis, the strongest association between IFN-γ levels and GCA risk was seen in those with samples taken nearer to the time of diagnosis. The OR was a significant 2.37 for those sampled 0.3–8.5 years prediagnosis (quartile 1), and was no longer statistically significant in those with a longer interval.

None of the other preselected biomarkers were significantly associated with the development of GCA overall, but CXCL-10 levels were significantly higher in people from quartile 1 who did versus did not develop GCA, as were CXCL-11 levels in quartile 2 (8.5–11.9 years prior to diagnosis).

The team says that all of these findings lost statistical significance after adjustment for multiple testing, but “the fact that several biomarkers are involved in the same inflammatory pathways increase the likelihood that they reflect important biologic mechanisms.”

Wadström et al also carried out a hypothesis-generating investigation using principal component analysis to evaluate a wider range of biomarkers. This revealed a number of proteins that were significantly associated with GCA risk when analyzed per standard deviation, including IFN-γ, CXCL-10, and CXCL-11 as in the prespecified analysis, as well as others including stem cell factor, interleukin-2 and -10RB, CXCL9, and monocyte chemotactic protein 3.

Taken together, the “[r]esults of the current study indicate that some of the processes in the pathogenesis of GCA may begin years before clinical onset,” write the researchers.

They highlight the need for validation studies to test whether their findings are generalizable to other populations.

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